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import tweepy
import csv
import pandas as pd
####input your credentials here
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)

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def getFeatureVector(tweet):
    featureVector = []
    #split tweet into words
    words = tweet.split()
    for w in words:
        #replace two or more with two occurrences
        w = replaceTwoOrMore(w)
        #strip punctuation
        w = w.strip('\'"?,.')
        #check if the word stats with an alphabet

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###Preprocess tweets
def processTweet2(tweet):
    # process the tweets

    #Convert to lower case
    tweet = tweet.lower()
    #Convert www.* or https?://* to URL
    tweet = re.sub('((www\.[^\s]+)|(https?://[^\s]+))','URL',tweet)
    #Convert @username to AT_USER
    tweet = re.sub('@[^\s]+','AT_USER',tweet)

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import nltk
training_set = nltk.classify.util.apply_features(extract_features, tweets)
# Train the classifier Naive Bayes Classifier
NBClassifier = nltk.NaiveBayesClassifier.train(training_set)
#ua is a dataframe containing all the united airline tweets
ua['sentiment'] = ua['tweets'].apply(lambda tweet: NBClassifier.classify(extract_features(getFeatureVector(processTweet2(tweet)))))

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import numpy as np

def Kittler(im, out):
    """
    The reimplementation of Kittler-Illingworth Thresholding algorithm by Bob Pepin
    Works on 8-bit images only
    Original Matlab code: https://www.mathworks.com/matlabcentral/fileexchange/45685-kittler-illingworth-thresholding
    Paper: Kittler, J. & Illingworth, J. Minimum error thresholding. Pattern Recognit. 19, 41–47 (1986).
    """
    h,g = np.histogram(im.ravel(),256,[0,256])

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var sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD');
var poly = ee.Geometry.Polygon(
        [[[-95.83648681640625, 29.561512529746743],
          [-95.042724609375, 29.57345707301757],
          [-95.02899169921875, 30.099989515377835],
          [-95.82275390625, 30.10711788709236]]]);
var spatialFiltered = sentinel1.filter(ee.Filter.eq('instrumentMode', 'IW')).filterBounds(poly)
                               .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
                               .select('VV');
var image = spatialFiltered.filterDate('2017-08-25', '2017-09-05').mosaic().clip(poly);

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/**
 * # Histogram matching
 * # Google Earth Engine with S1 SAR
 * # A very small experiment :D
 * # Author: Francis Laclé
 * # Year: 2019
 */ 

/**
 * Function that returns a normalized image using unitScale()